 Chapter 7.7.1: Describing colleges. Popular magazines rank colleges and universiti...
 Chapter 7.7.2: Affording college. From time to time, the Department of Education e...
 Chapter 7.7.3: Changes in how we watch. Movies earn income from many sources other...
 Chapter 7.7.4: What we watch now. The previous exercise looked at movie studio inc...
 Chapter 7.7.5: Growing icicles. Table 4.2 (page 98) gives data on the growth of ic...
 Chapter 7.7.6: Weights arent Normal. The heights of people of the same sex and sim...
 Chapter 7.7.7: Returns on stocks arent Normal. The 99.7 part of the 689599.7 rule ...
 Chapter 7.7.8: Remember what you ate. How well do people remember their past diet?...
 Chapter 7.7.9: Cicadas as fertilizer? Every 17 years, swarms of cicadas emerge fro...
 Chapter 7.7.10: Hot mutual funds? Investment advertisements always warn that pastpe...
 Chapter 7.7.11: More about cicadas. Lets examine the distribution of seed mass for ...
 Chapter 7.7.12: More on hot funds. Continue your study of the returns for Fidelity ...
 Chapter 7.7.13: Outliers? In Exercise 7.11, you noticed that the smallest and large...
 Chapter 7.7.14: Where does the water go? Here are data on the amounts of water with...
 Chapter 7.7.15: Bestselling soft drinks. Here are data on the market share of the ...
 Chapter 7.7.16: Presidential elections. Here are the percents of the popular vote w...
 Chapter 7.7.17: The Mississippi River. Table 7.1 gives the volume of water discharg...
 Chapter 7.7.18: More on the Mississippi River. The data in Table 7.1 are a time ser...
 Chapter 7.7.19: A big toe problem. Hallux abducto valgus (call it HAV) is a deforma...
 Chapter 7.7.20: More on a big toe problem. The HAV angle data in the previous exerc...
 Chapter 7.7.21: Predicting foot problems. Metatarsus adductus (call it MA) is a tur...
 Chapter 7.7.22: Predicting foot problems, continued.(a) Find the equation of the le...
 Chapter 7.7.23: Data on mice. For a biology project, you measure the tail length (c...
 Chapter 7.7.24: Catalog shopping (optional). What is the most important reason that...
 Chapter 7.7.25: 7.25 How are schools doing? (optional) The nonprofit group Public A...
 Chapter 7.7.26: Weighing bean seeds. Biological measurements on the same species of...
 Chapter 7.7.27: Breaking bolts. Mechanical measurements on supposedly identical obj...
 Chapter 7.7.28: Scatterplot. Plot the weight of the bar of soap against day. Is the...
 Chapter 7.7.29: Regression. Find the equation of the leastsquares regression line ...
 Chapter 7.7.30: Prediction? Use the regression equation in the previous exercise to...
 Chapter 7.7.31: Statistics for investing. Joes retirement plan invests in stocks th...
 Chapter 7.7.32: Initial public offerings. The business magazine Forbes reports that...
 Chapter 7.7.33: Moving in step? One reason to invest abroad is that markets in diff...
 Chapter 7.7.34: Interpreting correlation. The same article that claims that the cor...
 Chapter 7.7.35: Coaching for the SATs. A study finds that high school students who ...
 Chapter 7.7.36: Change in the Serengeti. Longterm records from the Serengeti Natio...
 Chapter 7.7.37: Prey attract predators. Here is one way in which nature regulates t...
 Chapter 7.7.38: Extrapolation. Your work in Exercise 7.36 no doubt included a regre...
 Chapter 7.7.39: When does the ice break up? We have 89 years of data on the date of...
 Chapter 7.7.40: Global warming? Because of the high stakes, the falling of the trip...
 Chapter 7.7.41: More on global warming. Sidebyside boxplots offer a different loo...
 Chapter 7.7.42: Save the eagles. The pesticide DDT was especially threatening to ba...
 Chapter 7.7.43: Thin monkeys, fat monkeys. Animals and people that take in more ene...
 Chapter 7.7.44: 7.45 Weeds among the corn. Lambsquarter is a common weed that inte...
 Chapter 7.7.45: Weeds among the corn, continued. We can also use regression to anal...
 Chapter 7.7.46: Is Old Faithful Faithful? Write a response to Questions 1 and 3 for...
 Chapter 7.7.47: Checkmating and Reading Skills. Write a report based on Question 1 ...
 Chapter 7.7.48: Counting Calories. Respond to Questions 1, 4, and 6 for this case s...
 Chapter 7.7.49: Mercury in Floridas Bass. Respond to Question 5. (Scatterplots, for...
 Chapter 7.7.50: Brain Size and Intelligence. Write a response to Question 3. (Scatt...
 Chapter 7.7.51: Acorn Size and Oak Tree Range. Write a report based on Questions 1 ...
 Chapter 7.7.52: Surviving the Titanic. Answer Questions 1, 2, and 3. (Twoway tables.)
Solutions for Chapter Chapter 7: Exploring Data: Part I Review
Full solutions for The Basic Practice of Statistics  4th Edition
ISBN: 9780716774785
Solutions for Chapter Chapter 7: Exploring Data: Part I Review
Get Full SolutionsThe Basic Practice of Statistics was written by and is associated to the ISBN: 9780716774785. This textbook survival guide was created for the textbook: The Basic Practice of Statistics, edition: 4. Since 52 problems in chapter Chapter 7: Exploring Data: Part I Review have been answered, more than 11152 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Chapter Chapter 7: Exploring Data: Part I Review includes 52 full stepbystep solutions.

Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.

Average
See Arithmetic mean.

Backward elimination
A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

Bivariate normal distribution
The joint distribution of two normal random variables

Block
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

Central tendency
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

Coeficient of determination
See R 2 .

Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

Components of variance
The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

Conditional mean
The mean of the conditional probability distribution of a random variable.

Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.

Conditional probability mass function
The probability mass function of the conditional probability distribution of a discrete random variable.

Correlation matrix
A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the offdiagonal elements rij are the correlations between Xi and Xj .

Counting techniques
Formulas used to determine the number of elements in sample spaces and events.

Crossed factors
Another name for factors that are arranged in a factorial experiment.

Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.

Density function
Another name for a probability density function

Estimate (or point estimate)
The numerical value of a point estimator.

Fraction defective control chart
See P chart

Hat matrix.
In multiple regression, the matrix H XXX X = ( ) ? ? 1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .